Laser line scan imagery has been found to have fluctuating brightness (contrast) regions due to high/low signal strengths when scanning data. High signal strength regions occur when the laser line scan sensor is perpendicular to a reflective surface (i.e. a sea bottom), where photons travel the shortest distance resulting in less scattering effects. Low signal strength regions occur at scan line off-angles and sudden drops of elevations in the reflective surface, where photons must travel longer resulting in more scattering effects. The low signal strength regions can limit the visibility of image details, allowing objects to "hide" within image "shadows". Consequently, greater contrast in the image is often required.
Typical contrast enhancement routines have difficulties enhancing the laser line scan images. This is because contrast enhancements are designed to enhance either low contrast regions or high contrast regions, but not both. Thus low contrast enhancement routines can be used to enhance the low signal strength regions, but not without deleterious effects to objects already visible in the high signal strength regions. Also, most contrast enhancement routines are pixel-based (such as a log scale enhancement) and cannot enhance local spatial variations such as the high/low signal fluctuations found in laser line scan data.
Histogram clipping, another commonly utilized enhancement technique, is also ineffective in enhancing laser line scan images. This is because histogram clipping enhances by removing noise pixels at the upper and lower ends of the image dynamic range. However, the high/low signal strength regions containing desired information about an image generally lie away from the extreme ends of the image dynamic range. Thus, since the separation between high and low signal strength regions within the image does not change, histogram clipping cannot effectively enhance information in the low signal strength regions which are suppressed by the high signal strength regions.
A least squares error method to estimate an image background as a means of image enhancement has been used before in the image art. In particular, a two-dimensional least squares error method to remove background "tilt" has been used before on laser line scan (LLS) data. However, this background estimate results in a planar surface which cannot equalize the "local"high/low intensity variations found in the laser line scan data.
The aforementioned two-dimensional method was first proposed by Dave Brown at Penn State University/Applied Research Laboratories in a report entitled Status Report: Electro-Optic Image Processing/Analysis, incorporated herein by reference to provide any necessary elaboration on the least squares error method. A modified version of this technique uses a one-dimensional least squares error method down to image columns and rows of a pixel array and is disclosed in the publication entitled Image Characterization of Target Recognition in the Surf Zone Environment, CSS/TR-96/19 by Andrew Nevis (the inventor of the present application), and is incorporated herein by reference. However, this technique cannot accurately estimate local intensity variations. Consequently, conventional LLS sensors do not operate at optimum theoretical efficiency, and this characterization imposes severe limitations on such devices as underwater sensors.